Early Fault Diagnosis of Gearbox Based on VWPSO-VMD

Cao, Shunxin; Li, Hongkun; Zhang, Kongliang; Wang, Zhaodong

doi:10.1088/1742-6596/2184/1/012006

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…The PSO [23], as a typical representative of swarm optimization algorithms, has the characteristics of easy to understand, easy to implement, strong global search ability and no human interference, which is suitable for key parameter optimization of FF-MD. Therefore, in this paper, we use the VWPSO [46] proposed in a previous study for optimization of the key parameters of FF-MD.…”

Section: Variable Weight Particle Swarm Algorithm Optimization (Vwpso...mentioning

confidence: 99%

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Li,

Cao,

Zhang

et al. 2024

Meas. Sci. Technol.

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Planetary gearboxes often experience multiple component failures during service, which can accelerate the degradation and failure of industrial equipment. Accurate separation and identification of multiple faults is an important means of ensuring the safe and stable operation of equipment. However, different faults can interact with each other, along with the influence of background noise, making it challenging to accurately extract faults with relatively weak energy among multiple faults. This difficulty leads to the problems of potential misdiagnosis and underdiagnosis. To address this issue, an adaptive mode decomposition method based on fault feature orientation (AMD-FF) is proposed in this paper. Initially, a fault impact indicator (FII) is constructed based on period-weighted kurtosis of envelope spectral and correlated combination negentropy to effectively characterize the impulsiveness and periodicity of fault features. Furthermore, with the objective of maximizing the FII, an adaptive decomposition of the original signal is designed based on blind convolution theory using a finite-impulse response filter group. Subsequently, a variable weight particle swarm optimization is employed to adaptively optimize the key decomposition parameters. Finally, the data of industrial-grade planetary gear transmission test rig are collected to validate the proposed method for compound fault diagnosis of planetary gearboxes. The results indicate that the AFMD-FF can effectively separate and extract compound faults in planetary gearboxes, demonstrating superior fault separation and diagnostic performance compared to the fault mode decomposition (FMD) and adaptive FMD. This method offers a novel approach to diagnosing compound faults in rotating equipment in industrial scenarios.

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Section: Variable Weight Particle Swarm Algorithm Optimization (Vwpso...mentioning

confidence: 99%

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Li,

Cao,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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Enhancing bearing and gear fault diagnosis: A VMD-PSO approach with multisensory signal integration

Lourari,

El Yousfi,

Benkedjouh

et al. 2024

Journal of Vibration and Control

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In the domain of signal analysis for machinery health monitoring and fault diagnosis, this paper introduces a comprehensive methodology that integrates Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and advanced machine learning techniques. The primary objective of this framework is to establish a robust and precise approach for signal decomposition, determining the optimal number of Intrinsic Mode Functions (IMF), and calculating key indicators, including L2/L1, Hoyer Index, and Geometric Mean Improved Gini Index (GMIGI). The methodology initiates with VMD-based signal decomposition, followed by the utilization of PSO to identify the most appropriate number of IMFs for accurate feature extraction. Subsequently, each IMF’s performance is assessed by evaluating its correlation with the input signal, and the IMF with the highest Pearson coefficient is selected as the primary feature for diagnostic purposes. To ensure the robustness and comparability of these indicators, a standardization process is implemented. The standardized indicators are then employed for machinery fault diagnosis, utilizing a diverse set of machine learning algorithms such as support vector machines and discriminant analysis. The proposed methodology undergoes rigorous validation using vibration, acoustic, and current signals, providing a versatile solution for the condition monitoring and diagnosis of mechanical systems. For model validation, we utilize four datasets comprising two vibrational, one acoustic, and one electrical dataset. The experimental results affirm the effectiveness of our approach in accurately detecting and diagnosing faults, thereby contributing to the reliability and maintenance efficiency of industrial machinery.

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Early Fault Diagnosis of Gearbox Based on VWPSO-VMD

Cited by 2 publications

References 5 publications

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Enhancing bearing and gear fault diagnosis: A VMD-PSO approach with multisensory signal integration

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